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   "id": "59c9951b-074f-4dc7-96c2-8d5f1990ca32",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torchvision\n",
    "from torch.utils import data\n",
    "from torchvision import transforms\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.use_svg_display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0303e5c-0d84-414f-84d7-59a3e8eea061",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 26.4M/26.4M [00:05<00:00, 5.04MB/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29.5k/29.5k [00:00<00:00, 148kB/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.42M/4.42M [00:01<00:00, 3.32MB/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.15k/5.15k [00:00<00:00, 15.7MB/s]\n"
     ]
    }
   ],
   "source": [
    "# 通过ToTensor实例将图像数据从PIL类型变换成32位浮点数格式，\n",
    "# 并除以255使得所有像素的数值均在0～1之间\n",
    "trans = transforms.ToTensor()\n",
    "mnist_train = torchvision.datasets.FashionMNIST(\n",
    "    root=\"../data\", train=True, transform=trans, download=True)\n",
    "mnist_test = torchvision.datasets.FashionMNIST(\n",
    "    root=\"../data\", train=False, transform=trans, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "371ad731-c131-421f-bdc7-d93297ed458b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mnist_train), len(mnist_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1897200a-e226-4e9d-b633-1983128ffb9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f2d5feb-7e8d-40d4-9962-27a1605187ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fashion_mnist_labels(labels):  #@save\n",
    "    \"\"\"返回Fashion-MNIST数据集的文本标签\"\"\"\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47f68e65-3076-421c-9efe-548216c36faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  #@save\n",
    "    \"\"\"绘制图像列表\"\"\"\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    axes = axes.flatten()\n",
    "    for i, (ax, img) in enumerate(zip(axes, imgs)):\n",
    "        if torch.is_tensor(img):\n",
    "            # 图片张量\n",
    "            ax.imshow(img.numpy())\n",
    "        else:\n",
    "            # PIL图片\n",
    "            ax.imshow(img)\n",
    "        ax.axes.get_xaxis().set_visible(False)\n",
    "        ax.axes.get_yaxis().set_visible(False)\n",
    "        if titles:\n",
    "            ax.set_title(titles[i])\n",
    "    return axes"
   ]
  },
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   "execution_count": 8,
   "id": "aac77f99-9ca8-4f9d-8a91-c0ca662886ff",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1350x300 with 18 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))\n",
    "show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3afd8fd4-00a0-4065-a32f-2d920290bf79",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "\n",
    "def get_dataloader_workers():  #@save\n",
    "    \"\"\"使用4个进程来读取数据\"\"\"\n",
    "    return 4\n",
    "\n",
    "train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
    "                             num_workers=get_dataloader_workers())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa8b5bcf-44c2-4768-bccb-ff15026e8005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8.31 sec'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timer = d2l.Timer()\n",
    "for X, y in train_iter:\n",
    "    continue\n",
    "f'{timer.stop():.2f} sec'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf593d77-c282-433b-98dd-20dc18807fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_fashion_mnist(batch_size, resize=None):  #@save\n",
    "    \"\"\"下载Fashion-MNIST数据集，然后将其加载到内存中\"\"\"\n",
    "    trans = [transforms.ToTensor()]\n",
    "    if resize:\n",
    "        trans.insert(0, transforms.Resize(resize))\n",
    "    trans = transforms.Compose(trans)\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(\n",
    "        root=\"../data\", train=True, transform=trans, download=True)\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(\n",
    "        root=\"../data\", train=False, transform=trans, download=True)\n",
    "    return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
    "                            num_workers=get_dataloader_workers()),\n",
    "            data.DataLoader(mnist_test, batch_size, shuffle=False,\n",
    "                            num_workers=get_dataloader_workers()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "feefed7f-e0a5-4701-b237-0cbb49f5dbbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64\n"
     ]
    }
   ],
   "source": [
    "train_iter, test_iter = load_data_fashion_mnist(32, resize=64)\n",
    "for X, y in train_iter:\n",
    "    print(X.shape, X.dtype, y.shape, y.dtype)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a4d017e-e02f-493d-8c90-418c19d70440",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from IPython import display\n",
    "from d2l import torch as d2l\n",
    "\n",
    "batch_size = 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89199546-cd5a-4418-8659-321fa73718a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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